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Fault Tolerance in MapReduce: A Survey

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Book cover Resource Management for Big Data Platforms

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

MapReduce-based systems have emerged as a prominent framework for large-scale data analysis, having fault tolerance as one of its key features. MapReduce has introduced simple yet efficient mechanisms to handle different kinds of failures including crashes, omissions, and arbitrary failures. This contribution discusses in detail the types of failures in MapReduce systems and surveys the different mechanisms used in the framework for detecting, handling, and recovering from these failures. It also surveys the state-of-the-art optimization mechanisms to improve the fault tolerance in MapReduce, and in particular its open-source implementation Hadoop. Finally, it identifies the remaining challenges and open issues for building efficient fault tolerance mechanisms for MapReduce.

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Notes

  1. 1.

    This is not particularly true in the case of speculative execution, since it has proven to exhaust a considerable amount of resources, when executed on heterogeneous environments [39, 72] or when the system is going under failures [22].

  2. 2.

    Jeff Dean, one of the leading engineers in Google, said: (we) “lost 1600 of 1800 machines once, but finished fine”.

  3. 3.

    It is important to mention that, differently from [72] which considers tasks as stragglers, in the default paper of Google [19], a straggler is “a machine that takes an unusually long time to complete one of the last few map or reduce tasks in the computation”.

  4. 4.

    Spot instances are virtual machines resources in Amazon Web Services (WS), for which a user defines a maximum biding price that he/she is willing to pay. If there is no concurrence, the prices are lower and the possibility of using them is higher. But when the demand is higher, then Amazon WS has the right to stop your spot instances. If the spot instances are stopped by Amazon, the user does not pay, otherwise if the user decides to stop them before completing the normal hour, the user is obliged to pay for that consumption.

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Acknowledgments

The research leading to these results has received funding from the H2020 project reference number 642963 in the call H2020-MSCA-ITN-2014.

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  1. OEG, E.T.S. Ingenieros Informáticos, Universidad Politécnica de Madrid, Campus de Montegancedo s/n, 28660, Boadilla del Monte, Madrid, Spain

    Bunjamin Memishi & María S. Pérez

  2. Inria Campus Universitaire de Beaulieu, Rennes, 35042, Brittany, France

    Shadi Ibrahim & Gabriel Antoniu

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  2. Cracow University of Technology, Cracow, Poland

    Joanna Kołodziej

  3. Second University of Naples, Naples, Caserta, Italy

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Memishi, B., Ibrahim, S., Pérez, M.S., Antoniu, G. (2016). Fault Tolerance in MapReduce: A Survey. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_11

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